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2007.15849
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Solving inverse problems using conditional invertible neural networks
Journal of Computational Physics (JCP), 2020
31 July 2020
G. A. Padmanabha
N. Zabaras
AI4CE
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Papers citing
"Solving inverse problems using conditional invertible neural networks"
31 / 31 papers shown
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Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation
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Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview
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Jamal F. Husseini
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Using Autoencoders and AutoDiff to Reconstruct Missing Variables in a Set of Time Series
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On the Approximation of Bi-Lipschitz Maps by Invertible Neural Networks
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Jun Zou
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Variational Sequential Optimal Experimental Design using Reinforcement Learning
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Wanggang Shen
Jiayuan Dong
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Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides
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Ilias Bilionis
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Bi-fidelity Variational Auto-encoder for Uncertainty Quantification
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Osman Asif Malik
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Stephen Becker
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Oliver De Candido
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Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems
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VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems
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Conditional Injective Flows for Bayesian Imaging
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K. Kothari
Leonardo Salsi
Ali Aghababaei Harandi
Maarten V. de Hoop
Ivan Dokmanić
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Diagnosing and Fixing Manifold Overfitting in Deep Generative Models
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Uncertainty quantification for ptychography using normalizing flows
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Z. Di
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183
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Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models
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Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning
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G. A. Padmanabha
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Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks
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